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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
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Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Showing 61 to 68 of 68 (0.129 seconds)Spelling suggestions: "subject:"connectionism"" "subject:"connectionisme""
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Vývoj paradigmat výzkumu umělé inteligence / Evolution of Artificial Intelligence Research ParadigmsHostičková, Iva January 2014 (has links)
(in English): The purpose of this thesis is to describe developments of research in the field of artificial intelligence, from the point of view reflecting changes in current paradigms, and to analyze contemporary tendencies. This thesis systemically places the paradigm term into contexts of theoretical sciences and it explains in what way the term is being used. Further, the thesis describes artificial intelligence and several selected components. The thesis researches the basic paradigms of artificial intelligence - the symbolic and connectionistic paradigm, and is also researching new approaches and analyzing their beginnings and important development periods. The thesis analyzes reasons that were behind these developments. In addition to questions related to technical developments, financial support of selected research played an important role. The closing part of the thesis also analyzes reasons of current artificial intelligence expansion, worries connected to this expansion, and current research trends.
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In Defense of Representational Explanations for Connectionist SystemsLamb, Maurice J. 29 July 2010 (has links)
No description available.
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Thinking Machines: Approaches, Achievements and ConsequencesRiedel, Marion 08 May 2002 (has links)
The paper discusses the basics of Cognitive Science
and describes the achievements of research at the
field of Artificial Intelligence. / Die im Rahmen des Seminars "Language - Mind - Brain:
An Introduction to Psycholinguistics" der englischen
Sprachwissenschaft entstandene Arbeit befasst sich
mit den Grundlagen der Kognition und diskutiert
die Ergebnisse der Forschung auf dem Gebiet der
Künstlichen Intelligenz.
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Connectionist modelling in cognitive science: an exposition and appraisalJaneke, Hendrik Christiaan 28 February 2003 (has links)
This thesis explores the use of artificial neural networks for modelling cognitive processes. It presents an
exposition of the neural network paradigm, and evaluates its viability in relation to the classical, symbolic
approach in cognitive science. Classical researchers have approached the description of cognition by
concentrating mainly on an abstract, algorithmic level of description in which the information processing
properties of cognitive processes are emphasised. The approach is founded on seminal ideas about
computation, and about algorithmic description emanating, amongst others, from the work of Alan Turing
in mathematical logic. In contrast to the classical conception of cognition, neural network approaches are
based on a form of neurocomputation in which the parallel distributed processing mechanisms of the brain
are highlighted. Although neural networks are generally accepted to be more neurally plausible than their
classical counterparts, some classical researchers have argued that these networks are best viewed as
implementation models, and that they are therefore not of much relevance to cognitive researchers because
information processing models of cognition can be developed independently of considerations about
implementation in physical systems.
In the thesis I argue that the descriptions of cognitive phenomena deriving from neural network modelling
cannot simply be reduced to classical, symbolic theories. The distributed representational mechanisms
underlying some neural network models have interesting properties such as similarity-based representation,
content-based retrieval, and coarse coding which do not have straightforward equivalents in classical
systems. Moreover, by placing emphasis on how cognitive processes are carried out by brain-like
mechanisms, neural network research has not only yielded a new metaphor for conceptualising cognition,
but also a new methodology for studying cognitive phenomena. Neural network simulations can be lesioned
to study the effect of such damage on the behaviour of the system, and these systems can be used to study
the adaptive mechanisms underlying learning processes. For these reasons, neural network modelling is best
viewed as a significant theoretical orientation in the cognitive sciences, instead of just an implementational
endeavour. / Psychology / D. Litt. et Phil. (Psychology)
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| 65 |
通俗心理學作為理論或實踐？─取消式唯物論與工具論的爭論及一個嘗試的解決 / Folk Psychology as Theory or Practice?─The Debate Between Eliminative Materialism and Instrumentalism, and a Tentative Solution劉增平, Liu , Chen Pin Unknown Date (has links)
在當代心靈哲學的討論中，「通俗心理學」是指日常生活中人們對於其自身或其他人心理狀態與行為的常識性理解，它是以命題態度作為核心。「取消式唯物論」認為通俗心理學題錯誤的，將會被神經科學所取消。此主張最初為費耶阿本所提出，本論文所討論的取消式唯物論，則是指邱奇瀾及邱奇蘭所主張的版本。根據他們的論點，通俗心理學是一個經驗理論，由於它是無能與停希的，並且無法被成熟的神經科學化約，所以它的律則及本體論皆是錯誤的，未來人類將會使用一套神經科學的語詞來表達我們的心靈現象，使得通俗心理學被徹底取消。另一方面，工具論則認為通俗心理學並不是經驗理論，有關命題態度的歸屬僅僅是實用上的工具，於預測上有用，但並不描述內在的物理機制，以命題態度為組成部份的通俗心理學是社會實踐中多目標的工具，它根植於人類日常生活中，因此無法被神經科學所化約或取消。本論文企圖透過對取消式唯物論與工具論間，對通俗心理學的相互爭論，進一步嘗試對「通俗心理學作為一個理論或實踐？」這個課題作一個初步的回答。我們認為通俗心理學題一個深受社會文化及演化因素影響的常識概念架構，它不僅是一因果解釋理論，並且也是依賴社會文化網絡的實踐工具，應當以理論與實踐的兩種進路來加以探討。如果通俗心理學並不僅僅是一經驗理論，而是具有理論與實踐兩面向文明及演化的產物的話，則通俗心理學將不會面臨被神經科學徹底取消的命運。
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Connectionist modelling in cognitive science: an exposition and appraisalJaneke, Hendrik Christiaan 28 February 2003 (has links)
This thesis explores the use of artificial neural networks for modelling cognitive processes. It presents an
exposition of the neural network paradigm, and evaluates its viability in relation to the classical, symbolic
approach in cognitive science. Classical researchers have approached the description of cognition by
concentrating mainly on an abstract, algorithmic level of description in which the information processing
properties of cognitive processes are emphasised. The approach is founded on seminal ideas about
computation, and about algorithmic description emanating, amongst others, from the work of Alan Turing
in mathematical logic. In contrast to the classical conception of cognition, neural network approaches are
based on a form of neurocomputation in which the parallel distributed processing mechanisms of the brain
are highlighted. Although neural networks are generally accepted to be more neurally plausible than their
classical counterparts, some classical researchers have argued that these networks are best viewed as
implementation models, and that they are therefore not of much relevance to cognitive researchers because
information processing models of cognition can be developed independently of considerations about
implementation in physical systems.
In the thesis I argue that the descriptions of cognitive phenomena deriving from neural network modelling
cannot simply be reduced to classical, symbolic theories. The distributed representational mechanisms
underlying some neural network models have interesting properties such as similarity-based representation,
content-based retrieval, and coarse coding which do not have straightforward equivalents in classical
systems. Moreover, by placing emphasis on how cognitive processes are carried out by brain-like
mechanisms, neural network research has not only yielded a new metaphor for conceptualising cognition,
but also a new methodology for studying cognitive phenomena. Neural network simulations can be lesioned
to study the effect of such damage on the behaviour of the system, and these systems can be used to study
the adaptive mechanisms underlying learning processes. For these reasons, neural network modelling is best
viewed as a significant theoretical orientation in the cognitive sciences, instead of just an implementational
endeavour. / Psychology / D. Litt. et Phil. (Psychology)
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| 67 |
Der Mensch und die 'Künstliche Intelligenz': Eine Profilierung und kritische Bewertung der unterschiedlichen Grundauffassungen vom Standpunkt des gemäßigten RealismusEraßme, Rolf 11 1900 (has links)
After a short introduction concerning the problem of "Artificial Intelligence" (AI) the work continues with a summary of the state of the art.Thereafter, it goes on to profile four different basic scientific views of human beings and AI: symbolism, connectionism, biologism and physicalism. The emphasis is on the elucidation of anthropologically relevant statements to intelligence, spirit, thinking, perception, will, consciousness, self-consciousness, feelings and life.It is demonstrated that the basic views referred to represent greatly abbreviated and distorted pictures of human beings. Theories that do not go beyond the quantifiable level cannot adequately encompass the nature of relevant concepts and capabilities. That is above all because of the fact that generally a philosophical materialism is advocated, which considers the existence of intellectual substances impossible. For this reason a philosophical critique is necessary. The position of moderate and critical realism is advocated, whose anthropological statements are secured by epistemological and metaphysical investigations.The work comes to the conclusion that human beings cannot be understood symbolistically, connectionistically, biologistically or physicalistically. Man is a physical-intellectual entity, endowed with reason, a living social being. He is formed and led by his intellectual and therefore immortal soul, which gives him uniqueness, irreplaceability and the value of personhood. He is capable of thinking and thus of objective, abstract perception, and therefore is intelligent. Humans have an unfettered will, which, led by mental perception, is to be directed toward the good. They are moreover, through reflection, self-conscious. Humans live an intellectually determined life, which essentially differs, despite biological similarity, from that of animals and cannot possibly, due to its substantial superiority, have developed from animal life.All substantial anthropological abilities (such as intelligence, will, consciousness etc.) presuppose spirit. Because it is not within the power of human beings to create a simple substance such as spirit, a thinking, perceptive, intelligent, willing, self-conscious, sentient living being can at best be only technically imitated, modelled or simulated but never be reproduced, copied or created. The relationship of humans to AI is thus determined by an insuperable difference between their natures.
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Revision of an artificial neural network enabling industrial sortingMalmgren, Henrik January 2019 (has links)
Convolutional artificial neural networks can be applied for image-based object classification to inform automated actions, such as handling of objects on a production line. The present thesis describes theoretical background for creating a classifier and explores the effects of introducing a set of relatively recent techniques to an existing ensemble of classifiers in use for an industrial sorting system.The findings indicate that it's important to use spatial variety dropout regularization for high resolution image inputs, and use an optimizer configuration with good convergence properties. The findings also demonstrate examples of ensemble classifiers being effectively consolidated into unified models using the distillation technique. An analogue arrangement with optimization against multiple output targets, incorporating additional information, showed accuracy gains comparable to ensembling. For use of the classifier on test data with statistics different than those of the dataset, results indicate that augmentation of the input data during classifier creation helps performance, but would, in the current case, likely need to be guided by information about the distribution shift to have sufficiently positive impact to enable a practical application. I suggest, for future development, updated architectures, automated hyperparameter search and leveraging the bountiful unlabeled data potentially available from production lines.
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